To simulate the scenario where only a small part of the data has observed sensitive attributes, we reserve different ratios of attribute labels and regard the rest as samples without observed attribute labels.
The hidden dimension of different methods is 256.
Since the input of the Adult dataset is a feature vector while is a feature embedding sequence on on NewsRec, we use a dense layer as the decoder on Adult and use a GRU network as the decoder on NewsRec.
Adam [1]} is used for model training and the learning rate is 0.01 on Adult and 0.001 on NewsRec.
The dropout [2]} ratio is 0.2.
On the Adult dataset, following prior works [3]}, we use demographic parity (denoted as DP) and equalized opportunity (denoted as OPP) as the fairness metric and use income prediction accuracy as the performance metric.
On the NewsRec dataset, following [4]} we use AUC as the performance metric.
In addition, we consider two types of fairness metrics.
The first one is taken from [5]}, which uses the accuracy of gender prediction from the top 5 recommendation results to measure fairness.
A higher gender prediction accuracy means worse fairness because the recommendation results are more heavily influenced by sensitive attributes.
The second one is similar to equalized opportunity, which uses the AUC differences between users in different gender groups [6]} (denoted as \(\Delta \) -AUC).
A smaller \(\Delta \) -AUC value means better fairness.
On both datasets, we randomly sample 10% of training data as validation sets, and tune the hyperparameters of our approach and baselines on them.
We repeat each experiment 5 times with different random seeds and report the average scores.
<TABLE>